Module1:
Introduction- What is intelligence? Foundations of artificial intelligence (AI). History of AI;
Problem Solving- Formulating problems, problem types, states and operators, state space, search strategies.
Module2:
Informed Search Strategies- Best first search, A* algorithm, heuristic functions, Iterative deepening
A*(IDA), small memory A*(SMA); Game playing - Perfect decision game, imperfect decision game,
evaluation function, alpha-beta pruning
Module3
: Reasoning-Representation, Inference, Propositional Logic, predicate logic (first order logic), logical
reasoning, forward chaining, backward chaining; AI languages and tools - Lisp, Prolog, CLIPS
Module4:
Planning- Basic representation of plans, partial order planning, planning in the blocks world,
heirarchical planning, conditional planning, representation of resource constraints, measures, temporal
constraints
Module5:
Uncertainty - Basic probability, Bayes rule, Belief networks, Default reasoning, Fuzzy sets and
fuzzy logic; Decision making- Utility theory, utility functions, Decisiontheoretic expert systems.
Module 6
: Inductive learning - decision trees, rule based learning, current-best-hypothesis search, leastcommitment
search , neural networks, reinforcement learning, genetic algorithms; Other learning methods -
neural networks, reinforcement learning, genetic algorithms.
Module7:
Communication - Communication among agents, natural language processing, formal grammar,
parsing, grammar